Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection

  • Sheikh Shanawaz Mostafa
  • Fernando Morgado-Dias
  • Antonio G. Ravelo-García
S.I. : Advances in Bio-Inspired Intelligent Systems
  • 12 Downloads

Abstract

Obstructive sleep apnea is a disorder characterized by pauses in respiration during sleep. Due to this disturbance in breathing, there is a decrease in the oxygen saturation (SpO2) level. Thus, SpO2 can be used as a source of information for the automatic detection of apnea. Several solutions exist in the literature where different features are used. To find a better discriminant capacity, a subset of few features that obtains higher accuracy with the proper classifier is needed. To face this challenge, this work compares two different feature selection methods. The first one is a filter method named minimum redundancy maximum relevance, and the other one is called sequential forward search. These methods are tested with different classifiers. Two public datasets with 8 and 25 subjects are used to test and compare the performances of the different feature selection methods. A set of features for each classifier is obtained, and the results are compared with the previous work. The results found in this work show a good performance with respect to the state of the art and present a good option for apnea screening with low resources.

Keywords

Classification Feature section mRMR SFS Sleep apnea SpO2 

Notes

Acknowledgements

All the authors acknowledge the Portuguese Foundation for Science and Technology for their support through Projeto Estratégico LA 9—UID/EEA/50009/2013. S. S. Mostafa acknowledges ARDITI—Agência Regional para o Desenvolvimento e Tecnologia under the scope of the Project M1420-09-5369-FSE-000001—PhD Studentship.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Instituto Superior Técnico da Universidade de LisboaLisbonPortugal
  2. 2.MITI - Madeira Interactive Technologies InstituteFunchalPortugal
  3. 3.Universidade da MadeiraFunchalPortugal
  4. 4.Institute for Technological Development and Innovation in Communications (IDeTIC)University of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain

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